Matching-space Stereo Networks for Cross-domain Generalization
Matching-space Stereo Networks for Cross-domain Generalization
End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occur in unseen domains (e.g., when moving from synthetic to real scenes). In this paper we introduce a novel family of architectures, namely …